Total variation superiorization schemes in proton computed tomography image reconstruction

TL;DR

This study explores the use of total variation superiorization schemes as an add-on to iterative algorithms in proton computed tomography, improving image quality and noise reduction despite data inconsistencies.

Contribution

Introduces and evaluates TVS schemes integrated with DROP algorithm for pCT, demonstrating enhanced image resolution and reduced noise, especially with simplified, computationally efficient variants.

Findings

Superiority of TVS schemes over standard DROP in spatial and density resolution

Elimination of feasibility proximity check improves noise and halves reconstruction time

Best results achieved with once-per-block superiorization and no proximity check

Abstract

Purpose: Iterative projection reconstruction algorithms are currently the preferred reconstruction method in proton computed tomography (pCT). However, due to inconsistencies in the measured data arising from proton energy straggling and multiple Coulomb scattering, noise in the reconstructed image increases with successive iterations. In the current work, we investigated the use of total variation superiorization (TVS) schemes that can be applied as an algorithmic add-on to perturbation-resilient iterative projection algorithms for pCT image reconstruction. Methods: The block-iterative diagonally relaxed orthogonal projections (DROP) algorithm was used for reconstructing Geant4 Monte Carlo simulated pCT data sets. Two TVS schemes added on to DROP were investigated; the first carried out the superiorization steps once per cycle and the second once per block. Simplifications of these schemes, involving the elimination of the computationally expensive feasibility proximity checking step of the TVS framework, were also investigated. The modulation transfer function and contrast discrimination function were used to quantify spatial and density resolution, respectively. Results: With both TVS schemes, superior spatial and density resolution was achieved compared to the standard DROP algorithm. Eliminating the feasibility proximity check improved the image quality, in particular image noise, in the once-per-block superiorization, while also halving image reconstruction time. Overall, the greatest image quality was observed when carrying out the superiorization once-per-block and eliminating the feasibility proximity check. Conclusions: The low contrast imaging made possible with TVS holds a promise for its incorporation into our future pCT studies.